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Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615673/ https://www.ncbi.nlm.nih.gov/pubmed/34821634 http://dx.doi.org/10.3390/bios11110419 |
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author | Busaleh, Mariam Hussain, Muhammad Aboalsamh, Hatim A. Amin, Fazal-e- |
author_facet | Busaleh, Mariam Hussain, Muhammad Aboalsamh, Hatim A. Amin, Fazal-e- |
author_sort | Busaleh, Mariam |
collection | PubMed |
description | Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses. |
format | Online Article Text |
id | pubmed-8615673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86156732021-11-26 Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network Busaleh, Mariam Hussain, Muhammad Aboalsamh, Hatim A. Amin, Fazal-e- Biosensors (Basel) Article Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses. MDPI 2021-10-26 /pmc/articles/PMC8615673/ /pubmed/34821634 http://dx.doi.org/10.3390/bios11110419 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Busaleh, Mariam Hussain, Muhammad Aboalsamh, Hatim A. Amin, Fazal-e- Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title | Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title_full | Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title_fullStr | Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title_full_unstemmed | Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title_short | Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network |
title_sort | breast mass classification using diverse contextual information and convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615673/ https://www.ncbi.nlm.nih.gov/pubmed/34821634 http://dx.doi.org/10.3390/bios11110419 |
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